Is the Medical Oncology Workforce in Canada in Jeopardy? Findings from the Canadian Association of Medical Oncologists’ COVID-19 Impact Survey Series
Bibliographic record
Abstract
The COVID-19 (C19) pandemic introduced challenges in all areas of the Canadian healthcare system. Along with adaptations to clinical care environments, there was increasing concern about physician burnout during this time. The Canadian Association of Medical Oncologists (CAMO) has examined the effects of the pandemic on the medical oncology (MO) workforce. A series of four multiple choice web-based surveys distributed to MOs who were identified using the Royal College of Physicians and Surgeons directory and CAMO membership in May 2020 (S1), July 2020 (S2), December 2020 (S3), and March 2022 (S4). Descriptive analyses were performed for each survey, and a Chi-square test (α = 0.05) was used to assess factors associated with planned change in practice in S4. The majority of respondents work in a comprehensive cancer center S1/S2/S3/S4 (87%/86%81%/88%) and have been in practice >10 years (56%/61%/50%/64%). The most commonly reported personal challenges were physical (60%) and mental (60%) wellness. In S4, 47% of MOs reported dissatisfaction with their current work-life balance. In total, 83% reported that their workload has increased since the beginning of C19, and 51% of MOs reported their future career plans have been impacted by C19. In total, 56% of respondents are considering retiring or reducing total working hours in the next 5 years. Since the onset of the C19 pandemic, there are concerns identified with wellness, increasing workload, and job dissatisfaction among MOs, associated with experienced staff who have >10 years in practice. As rates of cancer prevalence rise and treatments become more complex, it is crucial to address the concerns raised in these surveys to ensure that we have a stable MO workforce in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".